DIP Project Topics

Image processing is the method by which the output of an image is enhanced by using different algorithms. The first look of an object catches our attention. Digital image processing has become an interesting field of research for researchers around the world. This is mainly because the internet has brought the world closer and created a huge business market. So anybody can buy anything from anywhere around the world.

In such a scenario image of your product is the first way in which you can attract your customers. Thus it becomes important to enhance your images by digital image processing methods. Following is a complete overview of DIP project topics. First, let us start with the aims of digital image processing.

Innovative DIP Project Topics


Image processing is not a new domain. People have been using digital image processing for the following purposes.

  • To enrich one’s visibility.
  • To analyze the details of an image.
  • Whatever is the format (binary, color, grayscale, multispectral) of the image, the image processing techniques can be employed to analyze and understand it.

Our experts have been researching DIP project topics for more than 20 years. They insist that understanding the model of image processing is the first step in DIP projects. As they had delivered more than 300+ projects in digital image processing, they are very much knowledgeable and proficient in solving any issues associated with it. So you can get the guidance of our experts for your digital image processing project. Now let us understand in detail the working of DIP projects.


The first step in working of digital image processing is to capture signals from any sensors (image) and convert them into digital images. After obtaining digital images, the following processes are performed.

  • Improvement in image clarity
  • Removal of artifacts and noises
  • Extraction of different properties of the image (scale, size, objects, etc.)

For instance, let us consider a histogram.

  • It is the most famous and simple tool for processing images.
  • The image quality is retained in the histogram.
  • Values of the pixels are explicit in the histogram.

So the characteristics (entropy illumination contrast signal to noise ratio) of the digital image can be easily modified using it. Our engineers are well experienced in using histograms for processing digital images. Not only histograms, but they are also experts in handling different image processing tools and techniques. So you can approach them for any sort of guidance related to DIP Project topics. Now let us look into digital image processing elements. 


Acquiring input, followed by storing and processing images and communicating by displaying the output, are the various elements or processes of digital image processing. As you might know, these processes are huge areas of scope for applications of recent technologies.

In the research of using advanced technologies for these processes, we should not compromise on the quality of images. Let us have an idea of some of the parameters used to assess the quality of an image.


  • Artefacts
  • Noise
  • Resolution of an image (spatial, temporal, contrast)

These are the parameters that can be used to define image quality.

As an example, we would highlight the effects of spatial resolution on image quality. Spatial resolution is the direct outcome of how clearly the two smaller objects lying very close to each other can be distinguished separately. By processing digital images, spatial resolution is changed, which meaning increasing noise.

So your digital image processing algorithm and method must be very specific in retaining the image quality. Therefore, choosing the best image processing technique, you can seek the advice of our experts. Now let us see about image quality estimation methods.


The existing methods for estimating the quality of images can be categorized as follows.

  • Objective methods
    • RR
    • FR
  • Metrics of Statistical error
    • AD
    • PSNR
    • MAE
    • NCC
    • MSE
    • MD
    • SC
  • Metrics based on HVS features
    • SIM
    • UIQIM
      • NR
    • Pixel
    • Hybrid
  • Subjective methods
    • Single stimulus
    • Double stimulus
  • DSIS

Our experts can give you all the basic practical Knowledge necessary for handling these methods of image quality estimation. Connect with them at any time for research guidance and support dip project topics.

Now Knowledge about the parameters used for assessing image quality is a must for doing DIP projects. We are giving you the details of the parameters used for assessing image quality below.


The following can be best described as image quality assessing parameters.

  • Kurtosis – represents image data on normal distribution
  • Entropy – quantity of data to be coded for algorithm attributed to compression
  • Lateral chromatic aberration – focusing image colors at particular distances.
  • Blemishes – sensor dust leading to marks in the image
  • Contrast sensitivity – distinguishable image-object contrast relation
  • Noise – occurs due to image density variation
  • Veiling glare – flaring due to reflection within lens elements
  • Distortion of the lens – lines get curved due to lens distortion
  • Reproducing tone – the brightness of the image before and after processing must be retained
  • Image gradient – color and intensity of the image with respect to the direction
  • Variance – distinguishes one pixel from another
  • ISO sensitivity and accuracy of exposure – easily determined by cameras adjusted manually
  • Light fall off – also called vignetting. It darkens the image edges
  • Spatial resolution – it is the function of point and spread factors
  • Color moire – occurs due to repeating spatial frequencies.
  • Sharpness – changes due to shaking of the camera, focus and sensor accuracy, Changes in the atmosphere.
  • Artefacts – loss, in contrast, noise, data compression, loss during transmission
  • Accuracy in color – color is the vague factor for image quality determination

These are the parameters that can be used to check the quality of images. All the projects that we delivered specifically give immense importance to these parameters. As a result, our projects were very successful. You can get in touch with our developers and know more about the technical details of the projects that we delivered. Now let us have some idea on research areas in digital image processing. 


The following are the research areas in digital image processing.

  • Technologies for biometrics
  • Robotic vision
  • Underwater processing of images
  • Visual systems of humans
  • Medical imaging

We provide your project and research assistance on all these topics. We often ask our customers to enrich their Knowledge of the latest technologies and ongoing researches on their topic. To stand by that point, we are providing the details of the latest DIP project topics below.


The latest research topics in digital image processing are given below,

  • Similarity detection between the skull and digital face
  • Detection of fake colorized image
  • Detection of JPEG grid (automatic)
  • Splicing (localization and detection)
  • Analysis of video evidence
  • Identifying and analyzing forensic writer
  • Image steganography (based on the secret key)
  • Digital image forensic (copy and move)
  • Detection of auto-image forgery

We provide assistance for research project development, thesis writing, assignment, and paper publication on all these topics. Connect with us to get more details. Our engineers are keeping them up to date by learning to work on the latest topics in image processing. As a result, they have acquired greater Knowledge in these fields. Now let us look into the tools for image processing. 



  • Simple CV – python wrapper (OpenCV)
  • Scikit – image (image processing algorithm collections)
  • PIL or Python Image Library – handling and processing of images


  • OpenCV Filters (basic to advanced image processing)
  • Standard Filters (inbuilt filters for processing)
  • MATLAB (allows for faster prototyping, easy to use, error handling and troubleshooting)
  • ImageJ Filters (skeletonization of image processing)
  • GPU Filters (OpenGL pixel shaders for GPU power harnessing)
  • DIY Filters (perform iterations and operations on images)


  • FilterForge (filters are created using dataflow programming language based on the node) – it is used as an adobe photoshop plugin and has about 10,000 DIP filters

Expert with us has worked using these processing tools. They have also registered huge success in their attempts to implement dip project topics. So you can connect with us at any time for any kind of research support in digital image processing projects.

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